Imagine driving down a busy street full of pedestrians, other cars, traffic signs and signals, and potential road hazards. In order to drive safely down this street, you must continuously monitor all of these stimuli, ready to react to any changes that require immediate response. Dealing with this complexity would be straightforward if the brain simultaneously represented all information in the environment. Recent evidence suggests, however, that one's representation of the visual environment is quite sparse; at any given time, only a small number of objects in the environment are represented. To understand how humans interact with a complex visual environment, it is critical to determine which aspects of the environment are included in its representation. The objective of this project is to investigate factors that guide the acquisition of visual information, and the nature of the resulting representation. The first specific aim of the project is to characterize the neural activity associated with the development of a scene representation. To do this, several experiments are proposed in which subjects view scenes while electrophysiological data are recorded; these data will be used to draw conclusions concerning the relationships among semantic processing, attention, and scene representation. A second aim is to use electrophysiological data in conjunction with eye movement recording to investigate semantic effects on attention and eye movement control. These effects have not previously been studied using tools that permit direct measurement of underlying processes; electrophysiological methods provide such tools. The third specific aim of the project is to examine the representation of dynamic, interactive environments. In a series of experiments, scene representation is examined within a simulated driving environment. This approach provides several theoretical advantages over the more traditional use of static scenes. The fourth aim is to determine how the acquisition and representation of peripheral information is affected by the properties of a distracting task. In addition to its contributions to our understanding of scene representation, these experiments may also have important implications for real-world problems, such as driving distraction or interface design.